RT Journal Article
SR Electronic(1)
A1 Lutu, P.E.N.
A1 Engelbrecht, A.P.
YR 2006
T1 A comparative study of sample selection methods for classification
JF South African Computer Journal,
VO 2006
IS 36
SP 69
OP 85
PB South African Computer Society (SAICSIT),
SN 1015-7999,
AB Sampling of large datasets for data mining is important for at least two reasons. The processing of large amounts of data results in increased computational complexity. The cost of this additional complexity may not be justifiable. On the other hand, the use of small samples results in fast and efficient computation for data mining algorithms. Statistical methods for obtaining sufficient samples from datasets for classification problems are discussed in this paper. Results are presented for an empirical study based on the use of sequential random sampling and sample evaluation using univariate hypothesis testing and an information theoretic measure. Comparisons are made between theoretical and empirical estimates.,
LA English,
UL https://journals.co.za/content/comp/2006/36/EJC28009